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Are you thinking of getting a pet? How about a robotic pet dog?
The idea of a robotic canine companion is awesome, but imagine what one could do for someone with visual impairment. There’s been a focus on health and well-being at Qualcomm® HackMobile this year. We saw some great projects from our interns at Qualcomm HackMobile 2016, and Team Chasers backed this up big time with their Seeing Eye Robot project, which brought in an element of health and well-being.
Are you sitting comfortably, and with good posture? I hope so, because blog number three on Qualcomm® HackMobile is about to begin. We’ve been speaking to the talented participants of our intern hackathon, and getting the inside story on the projects they’ve created.
This is the second and final part of a guest post by Vladislav Shimanskiy, one of our Adreno™ engineers. His previous post explained the concepts behind an optimized implementation of device-side matrix multiply (MM) kernels and host-side reference code for Adreno 4xx and 5xx GPU families. In this post, he walks you through OpenCL listings you can use to implement the kernels and host code.
The matrix multiply (MM) operation has become very popular on GPUs thanks to recent interest in deep learning, which depends on convolutions. We’ve been hearing from developers who want to accelerate deep learning (DL) applications on Qualcomm® Snapdragon™ processors with Adreno™ GPUs.
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